A tibble is a table – a two dimensional data structure with rows (observations) and columns (variables).
I’ll use the terms observations and rows interchangeably depending on the context. The same goes for the terms variables and columns. As you may recall the datasets pulse and survey were of type tibble. Each variable in a tibble has a fixed type such as character, double etc. Let’s start by creating a tibble manually.
To create a tibble you need to make sure that the package tidyverse is installed and loaded. See installation for more details.
Enter the following to load tidyverse package:
library(tidyverse)
Creating a tibble is done using the keyword tibble taking a sequence of name=value pairs where:
name is the variable (column) name,value is the values of the observation.Take for example the variables name, year and colour to represent a person’s name, birth year and favourite colour:
favourite_colour <- tibble(name=c("Lucas","Lotte","Noa","Wim"),
year=c(1995,1995,1995,1994),
colour=c("Blue","Green","Yellow","Purple"))
When creating a tibble the column vectors must be of the same length.
The variable favourite_colour now holds the data. Enter its name in the R Console for inspection:
favourite_colour
# A tibble: 4 x 3
name year colour
<chr> <dbl> <chr>
1 Lucas 1995 Blue
2 Lotte 1995 Green
3 Noa 1995 Yellow
4 Wim 1994 Purple
AnswerWhat additional pieces of information do you see beside the content we provided?
‘# A tibble: 4 x 3’, which says that this is a tibble with dimensions 4x3 (4 observations and 3 variables),
the atomic type of each variable, in this case character and double,
the row numbers
Type the following to find out the dimensions of the tibble:
ncol(favourite_colour) # number of variables (columns)
[1] 3
nrow(favourite_colour) # number of observations (rows)
[1] 4
dim(favourite_colour) # dimensions : 4 rows and 3 columns
[1] 4 3
Show top and bottom rows of the tibble:
head(favourite_colour, 2) # first 2 observations (rows)
# A tibble: 2 x 3
name year colour
<chr> <dbl> <chr>
1 Lucas 1995 Blue
2 Lotte 1995 Green
tail(favourite_colour, 3) # last 3 observations (rows)
# A tibble: 3 x 3
name year colour
<chr> <dbl> <chr>
1 Lotte 1995 Green
2 Noa 1995 Yellow
3 Wim 1994 Purple
With the second argument to head and tail functions you can control the number of rows.
By default head and tail show 6 rows, i.e. when the second argument is omitted : head(favourite_colour) or tail(favourite_colour).
Often you may need to select certain variables, this can be done using square brackets [ :
favourite_colour["colour"]
# A tibble: 4 x 1
colour
<chr>
1 Blue
2 Green
3 Yellow
4 Purple
or combination of variables:
favourite_colour[c("name","year")]
# A tibble: 4 x 2
name year
<chr> <dbl>
1 Lucas 1995
2 Lotte 1995
3 Noa 1995
4 Wim 1994
Subset result of a tibble is always a tibble.
Selection of variables can also be achieved with indices as we saw in vectors:
favourite_colour[2:3]
# A tibble: 4 x 2
year colour
<dbl> <chr>
1 1995 Blue
2 1995 Green
3 1995 Yellow
4 1994 Purple
favourite_colour[c(1,3)]
# A tibble: 4 x 2
name colour
<chr> <chr>
1 Lucas Blue
2 Lotte Green
3 Noa Yellow
4 Wim Purple
To deselect use negative indices:
favourite_colour[-2]
# A tibble: 4 x 2
name colour
<chr> <chr>
1 Lucas Blue
2 Lotte Green
3 Noa Yellow
4 Wim Purple
If you want to work with variables as individual vectors then you can do this either by double square brackets or $ sign:
favourite_colour[["year"]]
[1] 1995 1995 1995 1994
favourite_colour$year
[1] 1995 1995 1995 1994
In some contexts (later in the course) it is convenient to use the function pull which does the same as [[ and $ :
pull(favourite_colour, year)
[1] 1995 1995 1995 1994
Tibbles can be written to data files and read back again. Many data formats exist but for brevity we will be using comma-separated-value (csv) format in this course. The functions involved for this purpose are write_csv and read_csv (see data import cheat sheet).
let us now save our first tibble into a file in csv format:
write_csv(x = favourite_colour, file = "favourite_colour.csv")
favourite_colour tibble is written to favourite_colour.csv text file. You may inspect the file with any editor and it should look something like:
name,year,colour
Lucas,1995,Blue
Lotte,1995,Green
Noa,1995,Yellow
Wim,1994,Purple
This way we can permanently store our results in files for later use. We can now read the csv file back into a R environment variable, e.g. favourite_colour_csv :
favourite_colour_csv <- read_csv(file = "favourite_colour.csv")
── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────
cols(
name = col_character(),
year = col_double(),
colour = col_character()
)
read_csv gives a summary of the variables and their inferred types.
favourite_colour_csv
# A tibble: 4 x 3
name year colour
<chr> <dbl> <chr>
1 Lucas 1995 Blue
2 Lotte 1995 Green
3 Noa 1995 Yellow
4 Wim 1994 Purple
Often we have different data sets with i) the same set of variables or ii) same set of observations but different variables which we would like to combine:
bind_rows
Take for example the following two data sets with common variables name,year and colour:
favourite_colour1 <- tibble(name=c("Lucas","Lotte","Noa","Wim"),
year=c(1995,1995,1995,1994),
colour=c("Blue","Green","Yellow","Purple"))
favourite_colour1
# A tibble: 4 x 3
name year colour
<chr> <dbl> <chr>
1 Lucas 1995 Blue
2 Lotte 1995 Green
3 Noa 1995 Yellow
4 Wim 1994 Purple
favourite_colour2 <- tibble(name=c("Raul", "Isaac"),
year=c(1998,1998),
colour=c("Red", "Green"))
favourite_colour2
# A tibble: 2 x 3
name year colour
<chr> <dbl> <chr>
1 Raul 1998 Red
2 Isaac 1998 Green
then the following gives the combined tibble:
favourite_colour1_2 <- bind_rows(favourite_colour1, favourite_colour2)
favourite_colour1_2
# A tibble: 6 x 3
name year colour
<chr> <dbl> <chr>
1 Lucas 1995 Blue
2 Lotte 1995 Green
3 Noa 1995 Yellow
4 Wim 1994 Purple
5 Raul 1998 Red
6 Isaac 1998 Green
The function bind_rows treats tibbles as a collection of unordered variables. Let’s take the same data as in favourite_colour2 but change the order of variables year and colour
favourite_colour3 <- tibble(name=c("Raul", "Isaac"),
colour=c("Red", "Green"),
year=c(1998,1998))
favourite_colour3
# A tibble: 2 x 3
name colour year
<chr> <chr> <dbl>
1 Raul Red 1998
2 Isaac Green 1998
then combining favourite_colour1 and favourite_colour3 will yield the same results:
favourite_colour1_3 <- bind_rows(favourite_colour1, favourite_colour3)
favourite_colour1_3
# A tibble: 6 x 3
name year colour
<chr> <dbl> <chr>
1 Lucas 1995 Blue
2 Lotte 1995 Green
3 Noa 1995 Yellow
4 Wim 1994 Purple
5 Raul 1998 Red
6 Isaac 1998 Green
AnswerWhat about
bind_rows(favourite_colour3, favourite_colour1)?
favourite_colour3, the first argument to bind_rows.
Another consequence of treating tibbles as a collection of unordered variables is that there is no restriction on the given variables in the tibbles, with other words they might be identical sets of variables as was shown in the examples above but not necessarily:
favourite_colour4 <- tibble(name=c("Raul", "Isaac"),
colour=c("Red", "Green"),
year=c(1998,1998),
height= c(173, 179))
favourite_colour4
# A tibble: 2 x 4
name colour year height
<chr> <chr> <dbl> <dbl>
1 Raul Red 1998 173
2 Isaac Green 1998 179
favourite_colour1_4 <- bind_rows(favourite_colour1, favourite_colour4)
favourite_colour1_4
# A tibble: 6 x 4
name year colour height
<chr> <dbl> <chr> <dbl>
1 Lucas 1995 Blue NA
2 Lotte 1995 Green NA
3 Noa 1995 Yellow NA
4 Wim 1994 Purple NA
5 Raul 1998 Red 173
6 Isaac 1998 Green 179
bind_cols
Let us now combine another tibble with variable ‘height’ for the same set of observations in favourite_colour1_2:
heights <- tibble(height=c(173, 179, 167, 181 , 173, 184))
heights
# A tibble: 6 x 1
height
<dbl>
1 173
2 179
3 167
4 181
5 173
6 184
bind_cols(favourite_colour1_2,heights)
# A tibble: 6 x 4
name year colour height
<chr> <dbl> <chr> <dbl>
1 Lucas 1995 Blue 173
2 Lotte 1995 Green 179
3 Noa 1995 Yellow 167
4 Wim 1994 Purple 181
5 Raul 1998 Red 173
6 Isaac 1998 Green 184
bind_cols expects the same number of obsersavations in each tibble, it is an error otherwise, and you as the user are responsible for the order of observations in each tibble.
Now inspect the type of the tibble we just created:
class(favourite_colour)
[1] "tbl_df" "tbl" "data.frame"
A tibble is in its core a ‘data.frame’, a base R data structure.
‘The types tbl_df’ and ‘tbl’ enforce additional convinient behaviours specific to tibble.
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